Brugia Malayi - Clark Science Center - Smith College

Developing and Evaluating a Monthly Curve Number Method for
Predicting Stormflow
Kate Meyer and Julia Signell
When rain falls on any surface, some is caught by the tree canopy, some infiltrates into the soil, and some runs off overland.
The portion that flows overland reaches the stream shortly after hitting the ground, so this water is called stormflow. Predicting
stormflow is an essential tool for addressing hydrologic problems such as flood forecasting, sedimentation, and nutrient loading.
Over the past few decades the Natural Resources Conservation Service’s Curve Number method has become one of the most
widely used tools for predicting stormflow. This method uses antecedent wetness conditions, soil type, and land use information to
assign Curve Numbers (CN) to watersheds. With a simple equation, CN and daily precipitation data can predict stormflow. 1
The simplicity of this method is appealing, unfortunately the CN method was designed for a daily time step and sometimes
precipitation data are only available by month. Using the assumption that the depth of rainfall behaves exponentially, we modified
the CN method to allow for monthly precipitation data. With this adaptation, the user can predict stormflow for a basin knowing
only total monthly precipitation, CN, and the number of storms each month.
We evaluated this tool by comparing the measured stormflow (‘observed stormflow’) to three different predicted stormflows.
We calculated one of the predicted stormflows by assuming that all of the storms in any given month had the same depth of
precipitation (‘simulated monthly mean’). We calculated the other two predicted stormflows using the new tool (‘simulated monthly
ex.’), and the standard daily CN method (‘simulated daily’). We compared the observed and predicted stormflows using two
different statistical measures. The coefficient of variation of the root mean squared error, CV(RMSE), measures how well the
graphs of predicted and observed stormflow match in shape. The annual percent error (APE) measures how well the predicted
total runoff for the year matches observed. We ran these tests at four USGS sites where we were able to calculate a meaningful
CN.
We determined that, except for a slight discrepancy at Curlew Creek in the CV(RMSE), daily works best, then exponential,
then mean. This suggests that our modification works fairly well and certainly better than simply using the average. Given the
exciting first findings of this monthly CN method, a possible avenue of future research might be to find and assess more sites
in varying climates across the United States or the world. (Supported by the Gordon and Betty Moore Foundation, Stanford
University and the Stephen Bechtel Fund, CEEDS)
Advisor: Andrew Guswa
References:
1
Dingman, S. Lawrence. Physical Hydrology. 2 nd edition. Long Grove, IL: Waveland Press Inc. 2008, 445-450.
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